Abstract: Disclosed are integrated circuit structures with buried rails and backside metals for routing input signals to and/or output signals from one or more cells of the integrated circuit structures. Port landing-free connections to input ports and/or from output ports are enabled. As a result, signal routing flexibility is enhanced.

Abstract: In one embodiment, a method of simulating an operation of an artificial neural network on a binary neural network processor includes receiving a binary input vector for a layer including a probabilistic binary weight matrix and performing vector-matrix multiplication of the input vector with the probabilistic binary weight matrix, wherein the multiplication results are modified by simulated binary-neural-processing hardware noise, to generate a binary output vector, where the simulation is performed in the forward pass of a training algorithm for a neural network model for the binary-neural-processing hardware.

Abstract: An integrated circuit includes a trench power rail to reduce resistance in a power rail or avoid an increase in resistance of a power rail as a result of the metal tracks being reduced in size as the technology node size is reduced. The trench power rail is formed in isolation regions between cell circuits. A cell isolation trench in the isolation region provides additional volume in which to dispose additional metal material for forming the trench power rail to increase its cross-sectional area. The trench power rail extends through a via layer to a metal layer, including signal interconnects. The trench power rail extends in a width direction out of the cell isolation trench in the via layer to couple to trench contacts of the adjacent cell circuits without vertical interconnect accesses (vias). A high-K dielectric layer can selectively isolate the trench power rail from the cell circuits.

Abstract: A compute-in-memory bitcell is provided that includes a pair of cross-coupled inverters for storing a stored bit. The compute-in-memory bitcell includes a logic gate for multiplying the stored bit with an input vector bit. An output node for the logic gate connects to a second plate of a capacitor. A first plate of the capacitor connects to a read bit line. A write driver controls a power supply voltage to the cross-coupled inverters, the first switch, and the second switch to capacitively write the stored bit to the pair of cross-coupled inverters.

Abstract: Certain aspects provide an apparatus for performing machine learning tasks, and in particular, to computation-in-memory architectures. One aspect provides a method for in-memory computation. The method generally includes: accumulating, via each digital counter of a plurality of digital counters, output signals on a respective column of multiple columns of a memory, wherein a plurality of memory cells are on each of the multiple columns, the plurality of memory cells storing multiple bits representing weights of a neural network, wherein the plurality of memory cells of each of the multiple columns correspond to different word-lines of the memory; adding, via an adder circuit, output signals of the plurality of digital counters; and accumulating, via an accumulator, output signals of the adder circuit.

Abstract: Compute-in-memory (CIM) bit cell array circuits include CIM bit cell circuits for multiply-accumulate operations. The CIM bit cell circuits include a memory bit cell circuit for storing a weight data in true and complement form. The CIM bit cell circuits include a true pass-gate circuit and a complement pass-gate circuit for generating a binary product of the weight data and an activation input on a product node. An RWL circuit couples the product node to a ground voltage for initialization. The CIM bit cell circuits also include a plurality of consecutive gates each coupled to at least one of the memory bit cell circuit, the true pass-gate circuit, the complement pass-gate circuit, and the RWL circuit. Each of the CIM bit cell circuits in the CIM bit cell array circuit is disposed in an orientation of a CIM bit cell circuit layout including the RWL circuit.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved performance through depth parallelism. One example neural-network-processing circuit generally includes a plurality of groups of processing element (PE) circuits, wherein each group of PE circuits comprises a plurality of PE circuits configured to process in parallel an input at a plurality of depths.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved handling of partial accumulation results in weight-stationary operations, such as operations occurring in compute-in-memory (CIM) processing elements (PEs). One example PE circuit for machine learning generally includes an accumulator circuit, a flip-flop array having an input coupled to an output of the accumulator circuit, a write register, and a first multiplexer having a first input coupled to an output of the write register, having a second input coupled to an output of the flip-flop array, and having an output coupled to a first input of the first accumulator circuit.

Abstract: Digital compute-in-memory (DCIM) bit cell circuit layouts and DCIM array circuits for multiple operations per column are disclosed. A DCIM bit cell array circuit including DCIM bit cell circuits comprising exemplary DCIM bit cell circuit layouts disposed in columns is configured to evaluate the results of multiple multiply operations per clock cycle. The DCIM bit cell circuits in the DCIM bit cell circuit layouts each couples to one of a plurality of column output lines in a column. In this regard, in each cycle of a system clock, each of the plurality of column output lines receives a result of a multiply operation of a DCIM bit cell circuit coupled to the column output line. The DCIM bit cell array circuit includes digital sense amplifiers coupled to each of the plurality of column output lines to reliably evaluate a result of a plurality of multiply operations per cycle.

Abstract: Certain aspects provide an apparatus for performing machine learning tasks, and in particular, to computation-in-memory architectures. One aspect provides a circuit for in-memory computation. The circuit generally includes: a plurality of memory cells on each of multiple columns of a memory, the plurality of memory cells being configured to store multiple bits representing weights of a neural network, wherein the plurality of memory cells on each of the multiple columns are on different word-lines of the memory; multiple addition circuits, each coupled to a respective one of the multiple columns; a first adder circuit coupled to outputs of at least two of the multiple addition circuits; and an accumulator coupled to an output of the first adder circuit.

Abstract: Certain aspects generally relate to performing machine learning tasks, and in particular, to computation-in-memory architectures and operations. One aspect provides a circuit for in-memory computation. The circuit generally includes multiple bit-lines, multiple word-lines, an array of compute-in-memory cells, and a plurality of accumulators, each accumulator being coupled to a respective one of the multiple bit-lines. Each compute-in-memory cell is coupled to one of the bit-lines and to one of the word-lines and is configured to store a weight bit of a neural network.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, a hybrid architecture that includes both neural processing unit (NPU) and compute-in-memory (CIM) elements. One example neural-network-processing circuit generally includes a plurality of CIM processing elements (PEs), a plurality of neural processing unit (NPU) PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs. One example method for neural network processing generally includes processing data in a neural-network-processing circuit comprising a plurality of CIM PEs, a plurality of NPU PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs; and transferring the processed data between at least one of the plurality of CIM PEs and at least one of the plurality of NPU PEs via the bus.

Abstract: An apparatus includes first and second compute-in-memory (CIM) arrays. The first CIM array is configured to store weights corresponding to a filter tensor, to receive a first set of activations corresponding to a first receptive field of an input, and to process the first set of activations with the weights to generate a corresponding first tensor of output values. The second CIM array is configured to store a first copy of the weights corresponding to the filter tensor and to receive a second set of activations corresponding to a second receptive field of the input. The second CIM array is also configured to process the second set of activations with the first copy of the weights to generate a corresponding second tensor of output values. The first and second compute-in-memory arrays are configured to process the first and second receptive fields in parallel.

Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

Abstract: In one embodiment, an electronic device includes a compute-in-memory (CIM) array that includes a plurality of columns. Each column includes a plurality of CIM cells connected to a corresponding read bitline, a plurality of offset cells configured to provide a programmable offset value for the column, and an analog-to-digital converter (ADC) having the corresponding bitline as a first input and configured to receive the programmable offset value. Each CIM cell is configured to store a corresponding weight.

Abstract: Digital compute-in-memory (DCIM) bit cell circuit layouts and DCIM array circuits for multiple operations per column are disclosed. A DCIM bit cell array circuit including DCIM bit cell circuits comprising exemplary DCIM bit cell circuit layouts disposed in columns is configured to evaluate the results of multiple multiply operations per clock cycle. The DCIM bit cell circuits in the DCIM bit cell circuit layouts each couples to one of a plurality of column output lines in a column. In this regard, in each cycle of a system clock, each of the plurality of column output lines receives a result of a multiply operation of a DCIM bit cell circuit coupled to the column output line. The DCIM bit cell array circuit includes digital sense amplifiers coupled to each of the plurality of column output lines to reliably evaluate a result of a plurality of multiply operations per cycle.

Abstract: Certain aspects provide a circuit for in-memory computation. The circuit generally includes an in-memory computation array having a plurality of computation circuits, each of the computation circuits being configured to perform a dot product computation. In certain aspects, each of the computation circuits includes a memory cell, a capacitive element, a precharge transistor coupled between an output of the memory cell and the capacitive element, and a read transistor coupled between a read bit line (RBL) and the capacitive element.

Abstract: Certain aspects provide a circuit for in-memory computation. The circuit generally includes a first memory cell, and a first computation circuit. The first computation circuit may include a first switch having a control input coupled to an output of the first memory cell, a second switch coupled between a node of the first computation circuit and the first switch, a control input of the second switch being coupled to a discharge word-line (DCWL), a capacitive element coupled between the node and a reference potential node, a third switch coupled between the node and a read bit-line (RBL), and a fourth switch coupled between the node and an activation (ACT) line.

Abstract: A bit cell circuit of a most-significant bit (MSB) of a multi-bit product generated in an array of bit cells in a compute-in-memory (CIM) array circuit is configured to receive a higher supply voltage than a supply voltage provided to a bit cell circuit of another bit cell corresponding to another bit of the multi-bit product. A bit cell circuit receiving a higher supply voltage increases a voltage difference between increments of an accumulated voltage, which can increase accuracy of an analog-to-digital converter determining a pop-count. A bit cell circuit of the MSB in the CIM array circuit receives the higher supply voltage to increase accuracy of the MSB which increases accuracy of the CIM array circuit output. A capacitance of a capacitor in the bit cell circuit of the MSB is smaller to avoid an increase in energy consumption due to the higher voltage.

Abstract: Certain aspects provide methods and apparatus for multiplication of digital signals. In accordance with certain aspects, a multiplication circuit may be used to multiply a portion of a first digital input signal with a portion of a second digital input signal via a first multiplier circuit to generate a first multiplication signal, and multiply another portion of the first digital input signal with another portion of the second digital input signal via a second multiplier circuit to generate a second multiplication signal. A third multiplier circuit and multiple adder circuits may be used to generate an output of the multiplication circuit based on the first and second multiplication signals.